# 4-Phase Orchestration: 5 Universal Agent Skills with YAML-Driven Rules, Composable Components, and Graceful Degradation > Source: > Published: 2026-07-01 12:34:54+00:00 When you're hard-coding your 3rd scoring if-else, maybe it's time to ask: can I move the rules into YAML and let the business change config instead of code? Every Agent developer faces the same dilemma — **every business scenario rewrites a similar pipeline**: **The skeleton is identical.** What changes is only the "content" at each step. Yet every team builds pipelines from scratch. [ teleagent-skills](https://github.com/yuzhaopeng-up/teleagent-skills) offers an answer: ``` ┌─────────────────────────────────────────────────────────────┐ │ Upper Business Skill │ │ (Scoring Engine / Evidence Chain / Data Aggregator / ...) │ └──────────┬──────────┬──────────┬──────────┬────────────────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌──────────┐┌──────────┐┌──────────┐┌──────────┐ │ Phase 1 ││ Phase 2 ││ Phase 3 ││ Phase 4 │ │ Extract ││ Analyze ││ Generate ││ Archive │ │ ││ ││ ││ │ │Info- ││Data- ││Report- ││Archive- │ │Extractor ││Analyst ││Generator ││Manager │ └────┬─────┘└────┬─────┘└────┬─────┘└────┬─────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌─────────────────────────────────────────────────┐ │ JSON Contract (Structured Data Contract) │ │ phase1_output.json → phase2_input.json → ... │ └─────────────────────────────────────────────────┘ ``` Core idea: **each Phase is an independent component, and Phases pass data only through JSON contracts**. ``` { "phase": "extract", "skill": "scoring-engine", "output": { "entities": [ { "name": "客户A", "type": "enterprise_customer", "attributes": { "annual_revenue": 50000000, "employee_count": 320, "industry": "制造" } } ], "metadata": { "extraction_time": "2026-07-01T10:30:00Z", "source": "CRM_API", "confidence": 0.92 } }, "next_phase": "analyze" } ``` The traditional approach hard-codes scoring rules: ``` # Hard-coded — every business change means a code change if customer.revenue > 10000000: score += 30 elif customer.revenue > 5000000: score += 20 ``` teleagent-skills does it differently — **all rules are externalized to YAML**: ``` # scoring_rules.yaml scoring_engine: name: "政企客户商机评分" version: "2.1" dimensions: - id: revenue name: "营收规模" weight: 0.30 rules: - condition: "attributes.annual_revenue >= 100000000" score: 30 label: "超大型" - condition: "attributes.annual_revenue >= 50000000" score: 20 label: "大型" - condition: "attributes.annual_revenue >= 10000000" score: 10 label: "中型" - id: industry name: "行业属性" weight: 0.25 rules: - condition: "attributes.industry in ['金融','医疗']" score: 25 label: "高价值行业" - id: growth name: "增长潜力" weight: 0.20 - id: connectivity name: "接入成熟度" weight: 0.15 - id: decision_chain name: "决策链清晰度" weight: 0.10 thresholds: high: 70 medium: 40 low: 0 ``` Business changed? Edit YAML. Need a new dimension? Add YAML. **Zero code changes.** All 5 Skills share the same 4-Phase skeleton, but each Skill's Phase behavior differs: | Skill | Phase 1 Extract | Phase 2 Analyze | Phase 3 Generate | Phase 4 Archive | |---|---|---|---|---| Scoring Engine | Extract scoring object attributes | Load YAML rules & match scores | Generate scoring report + recommendations | Archive scoring records | Evidence Chain | Extract evidence from multiple sources | Cross-validate + conflict detection | Generate evidence chain report | Archive validation records | Data Aggregator | Validate & clean raw data | Aggregation + YoY/MoM calculation | Output statistical report | Archive aggregated results | Visualization Renderer | Analyze data characteristics | Generate ECharts config | Render HTML/Dashboard | Cache chart assets | NL2Query | Extract query intent + entities | Build SQL + confidence assessment | Format query results | Record query logs | **The power of composability**: upper-level Skills can chain lower-level Skills on demand. For example, the data query gateway pipeline: ``` NL2Query(Phase1-2) → Data Aggregator(Phase1-2) → Visualization Renderer(Phase1-3) ``` One natural language query automatically flows through "understand → query → aggregate → visualize" end to end. The 4-Phase architecture has built-in 3-tier degradation: ``` ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Phase 1 │────▶│ Phase 2 │────▶│ Phase 3 │────▶│ Phase 4 │ │ Extract │ │ Analyze │ │ Generate │ │ Archive │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ │ │ ▼ ▼ ▼ ▼ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ Return │ │ Skip │ │ Simplify │ │ Local │ │ raw │ │ analysis │ │ template │ │ cache + │ │ input + │ │ mark low │ │ raw data │ │ deferred │ │ conf=0 │ │ confidence│ │ passthrough│ │ retry │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ ``` **The core principle of degradation**: I'd rather give the user a low-confidence result than crash with an error. **What it is**: A multi-dimensional weighted scoring component driven by YAML rule configs. **Typical scenarios**: enterprise opportunity scoring, vendor evaluation, customer churn prediction, partner tiering. Input → Rule matching → Scoring output flow: ``` { "customer": "客户A", "total_score": 78, "grade": "A级-重点跟进", "dimension_breakdown": { "revenue": { "score": 20, "max": 30, "label": "大型" }, "industry": { "score": 15, "max": 25, "label": "中价值行业" }, "growth": { "score": 20, "max": 20 }, "connectivity": { "score": 15, "max": 15 }, "decision_chain": { "score": 8, "max": 10 } }, "recommendation": "建议安排专属客户经理，优先推荐5G专网+云网融合方案" } ``` **What it is**: A multi-source evidence cross-validation component that detects conflicts, evaluates confidence, and pinpoints root causes. ``` Data Source 1: Customer complaints ──┐ │ ┌──────────────────┐ Data Source 2: System alert logs ──┼────▶│ Evidence Chain │ │ │ Phase2: Analyze │ Data Source 3: SLA monitoring data ──┘ └────────┬─────────┘ │ ┌───────────────────────┐│ │ Cross-validation: │ │ • Complaint: "2hr outage" │ • Alert: "optical attenuation"│ │ • SLA: "99.1% availability" │ │ Conflict detected: │ │ Complaint vs SLA surface contradiction│ │ Root cause="optical attenuation": 0.87│ └───────────────────────┘ ``` **What it is**: A raw data re-processing component supporting validation/cleaning, aggregation, YoY/MoM, and TOP rankings. ``` Raw query results Aggregator output ┌──────────────┐ ┌──────────────────────────────┐ │ 300 rows of │───────▶ │ Monthly summary + YoY/MoM │ │ detail data │ │ TOP10 ranking │ │ (region x │ │ Anomaly flags (>2σ) │ │ month) │ │ Trend direction │ └──────────────┘ └──────────────────────────────┘ ``` **What it is**: An automated rendering component that turns structured data into ECharts charts and Dashboards. Chart type selection is automatic: time-series → line chart, categorical → bar/pie, multi-dimensional → radar. **What it is**: A smart natural-language-to-structured-query conversion component. ``` User input: "华东区上月5G专网新增客户数" Phase1 Extract: intent="query", entities=[region="华东", time="上月", metric="5G专网"] Phase2 Analyze: Generate SQL + confidence 0.88 Phase3 Generate: Format results Phase4 Archive: Record query log ``` **Confidence scoring**: When confidence drops below a threshold, the output gets a "low confidence" warning and shows the SQL for human review. | Industry | Scoring Engine | Evidence Chain | Data Aggregator | Viz Renderer | NL2Query | |---|---|---|---|---|---| Finance | Customer credit rating | Anti-money-laundering multi-source verification | Transaction volume YoY/MoM | Risk control dashboard | "Check a customer's last 3 months of transactions" | Manufacturing | Supplier evaluation | QA vs. production line verification | Line OEE statistics | Capacity dashboard | "Check line A's yield this month" | Retail | Member value scoring | Data conflict detection | SKU sales aggregation | Sales heatmap | "Top sellers in East China" | Healthcare | Patient risk stratification | Diagnosis vs. lab verification | Department admission stats | Bed occupancy dashboard | "Available beds in cardiology" | **Key insight**: All 5 Skills adapt to different industries purely through YAML rule configs. The scoring engine code logic is identical — only the YAML files differ. | Dimension | LangChain/LlamaIndex | AutoGen/CrewAI | teleagent-skills | |---|---|---|---| | Orchestration | Code-level Chain | Multi-Agent dialogue | 4-Phase declarative orchestration | | Rule management | Hard-coded in code | Described in prompts | YAML parameterized config | | Degradation strategy | try-catch | Retry dialogue | Declarative degradation config | | Business adaptation | Change code | Change prompts | Change YAML | ``` git clone https://github.com/yuzhaopeng-up/teleagent-skills.git cd teleagent-skills # Scoring engine example cp skills/scoring-engine/config/scoring_rules.yaml my_rules.yaml # Edit my_rules.yaml to customize your scoring dimensions # Copy the Skill directory into your Agent platform's skills directory ``` License: **Apache 2.0** | Repo | What It Does | GitHub | |---|---|---| financial-ai-skills | Financial AI skill library: 104 scenarios, pure Python | | **Stop building pipelines from scratch. The 4-Phase skeleton is ready — you just need to write YAML.** Star [teleagent-skills](https://github.com/yuzhaopeng-up/teleagent-skills) and let's standardize Agent Skills together.